In most experiments, we follow the general setup and select ResNet12 [1]} as the backbone on mini-ImageNet and tiered-ImageNet.
Unlike traditional ResNet architecture [2]}, Dropblock regularizer (dropblock size = 2) [3]} is applied here.
For the optimizer, we select the stochastic gradient descent (SGD) with Nesterov momentum (\(0.9\) ) and introduce StepLR.
For the learning rate, we employ a weight decay of \(1e-4\)  with the initial value of \(0.05\) .
In the pre-training stage, the training epochs are set to 120.
In the meta-testing stage, it includes 600 episodes and tests 15 query samples per class for all the episodes.
In the few-shot classification, the selected classifier follows the default implementation of scikit-learn [4]}.
Besides, this classification step has no fine-tuning process.
For other detailed settings, such as the number of filters, the data pre-processing method, and the data augmentation method, please refer to [1]}.
